Method for identifying misallocated historical production data using machine learning to improve a predictive ability of a reservoir simulation

ABSTRACT

A method for training a predictive reservoir simulation in which high-confidence reservoir sample data is used to identify misallocated historical production data used in the simulation. A neural network algorithm is trained with high-confidence reservoir historical production data. High-confidence reservoir sample data is obtained by at least one sensor at a reservoir location over a time interval, after which the reservoir historical production data is parametrically varied over the time interval to determine a time-indexed discrepancy between the reservoir historical production data and the high-confidence reservoir sample data over the time interval. The time-indexed discrepancy and a defined threshold discrepancy are then used as inputs to a machine learning process to further train the neural network algorithm to identify reservoir historical production data whose discrepancy exceeds the threshold discrepancy and thereby constitutes misallocated historical production data. The misallocated data is later back allocated to respective wells by back propagation algorithm.

FIELD OF THE DISCLOSURE

This patent application relates to methods for improving a predictiveability of a reservoir simulation, and particularly to theidentification of misallocated historical production data that adverselyaffects the predictive ability of a reservoir simulation.

BACKGROUND OF THE DISCLOSURE

Reservoirs are complex geological features whose thermofluidicproperties are governed by myriad interacting physical phenomena, whichcan be described by the governing differential equations forconservation of mass (continuity), conservation of momentum(Navier-Stokes equations), and conservation of energy. As reservoirssuch as petroleum reservoirs are natural phenomena, it is usuallydifficult to develop closed-form, analytical solutions for the entireflow field merely by applying the classical conservation equations(i.e., mass, momentum, and energy) of fluid mechanics. For example, acomplete flow field solution to the Navier-Stokes equation for momentumin a fluid flow is often only analytically possible for a very simple,axisymmetric flow field geometry. The flow field over an entirepetroleum reservoir, on the other hand, is an exceedingly complexmultivariate problem replete with asymmetric, irregular geometries andflow variable interdependencies that defy solution using analyticalmethods.

The analytical intractability of the problem has spurred the developmentof certain analytical tools that are best implemented on digitalcomputers. Computational fluid dynamics (CFD) software packages are onetype of tool that finds widespread use in determining the thermofluidicproperties of complex flow systems. A CFD software package orapplication program encodes the governing differential equations into adigital computer simulation that, when supplied with input includinginitial and boundary conditions, can determine fluid velocities,pressures, and heat transfer rates at locations throughout the flowfield. Data to provide valid initial and boundary conditions can besupplied by downhole sensors such as production logging tools. As areservoir builds a production history, the data collected across thefull complement of sensors distributed therein will serve to validatethe CFD simulation as well as provide guidance to customize thesimulation to local conditions.

While being a powerful analytical tool, computational fluid dynamicssoftware still has limitations when applied to petroleum reservoirs. CFDis most ideally suited to the computational solution of channel flows(i.e., pipes) and free-stream flows about bodies that display at leastsome degree of symmetry, e.g., aircraft and missiles. Petroleumreservoirs not only involve fluid flows about and within more arbitrary,natural shapes, but they also add complex geological considerations suchas the porosity of the reservoir medium in which the petroleum to beextracted resides. The differential equations encoded into a standardCFD software package are not sufficient to account for the multitude ofcomplex variables that come into play as a petroleum reservoir issubjected to an extraction operation over time.

It is with respect to this background that the present disclosure isaddressed.

SUMMARY OF THE DISCLOSURE

The present disclosure provides a method and system that can be used toidentify misallocated historical production data present in a trainingset of data that has been used to train a neural network algorithm,thereby enabling corrections of the inputs to a reservoir simulationmodel to be made.

According to a method consistent with the present disclosure, acomputer-based method is provided whereby a neural network model istrained using high-confidence reservoir historical production data andhigh-confidence geological data, PVT saturation logs, portable tests,and other measurements to identify misallocated historical productiondata and thereby improve a predictive ability of the reservoirsimulation. According to this method, a neural network algorithm istrained with a reservoir training set to produce correlations thatenable the reservoir simulation to make predictions of reservoirperformance, wherein the reservoir training set comprises reservoirphysical conditions corresponding to reservoir historical productiondata obtained during operation of the reservoir. The method then uses atleast one portable sensor or at least one sensor at a reservoir locationto obtain high-confidence reservoir sample data over a time interval,for use in comparison to the reservoir historical production data intraining the algorithm to recognize outlier or misallocated historicalproduction data. Next, the method involves parametrically varying thereservoir historical production data over the time interval to determinea time-indexed discrepancy between the reservoir historical productiondata and the high-confidence reservoir sample data which has beenmeasured over the time interval. In turn, the time-indexed discrepancyis used in conjunction with a defined threshold discrepancy as inputs toa machine learning process to further train the neural network algorithmto identify misallocated historical production data, which is defined asreservoir historical production data whose discrepancy exceeds thethreshold discrepancy. Having identified any misallocated historicalproduction data that may be present in the reservoir simulation, it canthen be replaced in the reservoir simulation training set with thehigh-confidence reservoir sample data to produce a revised training set.

In certain implementations, the method involves retraining the neuralnetwork algorithm to produce correlations using the revised training setas input to the neural network algorithm.

In certain implementations, the step of retraining the neural networkalgorithm with the revised training set can be repeated, producingrevised correlations in support of improved predictions of reservoirperformance by the reservoir simulation. This allows new training runsto be made with the reservoir simulation that further increase theaccuracy of its predictions.

In certain implementations, the machine learning process whereby theneural network algorithm is trained to identify misallocated historicalproduction data comprises a neural network algorithm.

According to another method consistent with the present disclosure, acomputer-based method is provided for training a reservoir simulationusing reservoir historical production data and high-confidence reservoirsample data to identify and replace misallocated historical productiondata with high-confidence reservoir sample data, thereby improving apredictive ability of the reservoir simulation. High-confidencereservoir historical production data is obtained over an interval oftime from a plurality of sensors distributed throughout the reservoirand used as a training set to train a neural network algorithm tofunction as a reservoir simulation capable of predicting values forreservoir production data. High-confidence reservoir sample data is thenobtained using at least one portable sensor or at least one of theplurality of sensors distributed throughout the reservoir. Abackpropagation algorithm is used to compare the reservoir historicalproduction data to high-confidence reservoir sample data atcorresponding position and time points, and a revised training set iscompiled by replacing instances of reservoir historical production datawith high-confidence reservoir sample data at corresponding position andtime points at which differences between the two datasets, indicatingmisallocated historical production data, have been identified by thecomparison. The revised training set can then be used to retrain theneural network algorithm to improve the predictive ability of thereservoir simulation.

The present disclosure also provides a computer-based system that can beused to train a reservoir simulation based upon reservoir historicalproduction data to identify misallocated historical production data toimprove a predictive ability of the reservoir simulation. The systemincludes at least one processor connected to at least one memory, and areservoir simulation running on the at least one processor. Thereservoir simulation incorporates a neural network algorithm trainedwith a reservoir training set to generate correlations that enable thereservoir simulation to make predictions of reservoir performance. Thereservoir training set includes data related to reservoir physicalconditions that reflect reservoir historical production data obtainedpreviously by sensors in the reservoir during operations. The systemincludes at least one portable sensor or at least one sensor at areservoir location to provide high-confidence reservoir sample data overa time interval, which is to be used to check the quality of thepreviously-obtained reservoir historical production data. Sometimes newmeasuring techniques or equipment, once installed provides moreaccurate, high-confidence data measurements. This newly acquired datawill have high accuracy and the interval over which the new data iscollected can be used to train the model. By training the model withaccurate data, the system, through its machine learning, can discern theprevious data as being erroneous by virtue of it not fitting in theoperational window of the model.

As such, an expert system is also part of the disclosed system and runson the at least one processor. The expert system is configured toparametrically vary the reservoir historical production data over thetime interval to determine a time-indexed discrepancy between thereservoir historical production data and the high-confidence reservoirsample data which has been sensed over the time interval, asdiscrepancies of a certain size tend to be more indicative ofmisallocated or “outlier” data than they are an indication of meremeasurement uncertainty. A machine learning process also runs on the atleast one processor, and the machine learning process takes thetime-indexed discrepancy determined by the expert system and a definedthreshold discrepancy as inputs to further train the neural networkalgorithm to identify misallocated historical production data, which isdefined as data whose discrepancy exceeds the threshold discrepancy. Themachine learning process is also configured to replace instances ofidentified misallocated historical production data in the reservoirsimulation training set with the high-confidence reservoir sample datato produce a revised training set.

In one implementation consistent with the present disclosure, the neuralnetwork algorithm is configured to be retrained using the revisedtraining set as input, resulting in the generation of correlations bythe neural network algorithm.

In another implementation consistent with the present disclosure, theretraining of the neural network algorithm can be repeated with therevised training set, producing revised correlations in support ofimproved predictions of reservoir performance by the reservoirsimulation.

In a further aspect that may be included in implementations of thesystem, the machine learning process used to determine the time-indexeddiscrepancy comprises a neural network algorithm.

According to another system consistent with the present disclosure, acomputer-based system is provided wherein a reservoir simulation istrained using high-confidence reservoir historical production data andhigh-confidence reservoir sample data to identify misallocatedhistorical production data and replace misallocated historicalproduction data with high-confidence reservoir sample data, therebyimproving a predictive ability of the reservoir simulation. A pluralityof sensors is distributed throughout the reservoir to obtainhigh-confidence reservoir historical production data over an interval oftime. A system comprising at least one processor connected to at leastone memory has a reservoir simulation running on the at least oneprocessor. The reservoir simulation comprises a neural network algorithmtrained with a reservoir training set comprising the high-confidencereservoir historical production data to enable the neural networkalgorithm to function as a reservoir simulation capable of predictingvalues for reservoir production data. The neural network algorithmfurther comprises a backpropagation algorithm which is configured tocompare the reservoir historical production data to high-confidencereservoir sample data at corresponding position and time points, whereinthe high-confidence reservoir sample data has been obtained using atleast one portable sensor or at least one of the plurality of sensorsdistributed throughout the reservoir. The backpropagation algorithmcompiles a revised training set by replacing instances of reservoirhistorical production data with high-confidence reservoir sample data atcorresponding position and time points at which differences between thetwo datasets, indicating misallocated historical production data, havebeen identified by the comparison. The algorithm is trained with anoperational window for each well based on reservoir properties, facilityconstraints and its production potential limits. The backpropagationalgorithm then retrains the neural network algorithm using the revisedreservoir training set to improve the predictive ability of thereservoir simulation. The training can be accomplished utilizing aneural network algorithm, examples of which include convolutional neuralnetworks (CNN) or K-Nearest Neighbors Networks (KNN).

In certain implementations according to any of the foregoing systems andmethods, the reservoir historical production data and thehigh-confidence reservoir sample data can include, without limitation,observed production data, well logs, saturation logs, permeability logs,porosity logs, the product of formation permeability k and producingformation thickness h (kh), reservoir contact length, well spacing,choke opening, well location, well depth, well test data, welltrajectories, well workover data, buildup test data, production loggingtool data, repeat formation tester (RFT) data,pressure-volume-temperature (PVT) data, gas/oil ratio (GOR) data, coredata, special core analysis laboratory (SCAL) data, gas oil separationplant (GOSP) level, well level, well rate tests, separator tests,portable tests, and productivity/injectivity index. These measurementsare used in conjunction with known reservoir geology parameters to traina well performance model that defines an operational window for thewell, bounded by maximum and minimum performance limits.

For example, the fact that the reservoir rock possesses virtually noelasticity will cause any attempt to produce at higher rate to result ina high pressure drop. Measurement instrumentation distributed throughoutthe reservoir will measure the pressure changes and correspondingproduction rates, which are then correlated with the geology of the“tight” reservoir rock. Any wells that are close in proximity to thesubject well will exhibit similar behavior, and this fact is also usedin training the model.

As a further example, a production rate cannot be pushed higher whenoperating the well at the maximum draw down pressure, and theseconditions will define the upper limit of the well's production rate. Ifthe algorithm subsequently encounters a data value which falls outsideof the limits defined in the model, the data is identified asmisallocated and sent to a pool of misallocated data for subsequentreallocation by the algorithm to the appropriate well.

The time intervals used for the measurements which will be used toupdate the algorithm are preferably periods during which high-precisionsensors were in place to obtain the measurements. Data provided by thesehigh-precision sensors, wherein there is 95% confidence or better thatthe measurements are accurate, are called “high-confidence” data. Putanother way, “high-confidence” data refers to measurements from thefield wherein the actual conditions under which the measurement wastaken, such as the actual pressure and temperature conditions as well asthe precise location of the measurement, are known. During the servicelife of a given wells, operators will often have compiled recordscovering multiple periods over which the measurements are regarded asaccurate or “high-confidence” data. This repository of “high-confidence”data defines the operational window of these wells and can be used tofurther train the model to make determinations of misallocated data.

These and other features, aspects, and advantages can be appreciatedfrom the following description of certain embodiments in accordance withthe present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawing figures illustrate exemplary embodiments andare not intended to be limiting of the present disclosure. Among thedrawing figures, like references are intended to refer to like orcorresponding parts.

FIG. 1 illustrates a schematic view of one exemplary neural network usedby the reservoir simulation of the present disclosure;

FIG. 2A illustrates an exemplary production logging tool that is used toobtain reservoir historical production data related to reservoiroperation;

FIG. 2B illustrates an exemplary downhole well tractor tool that is usedto deploy a production logging tool;

FIG. 3 illustrates a schematic view of an exemplary petroleum reservoirincorporating wells and sensors used by the present disclosure, as wellas representative data output by the sensors;

FIG. 4. shows an exemplary graph of reservoir historical production datawith a “high-confidence” data interval from 2009 to 2016. The data hasthe status of “high confidence” data due to the fact that themeasurements were made under known conditions with new instrumentationthat was installed during this period.

FIG. 5 shows the exemplary graph of FIG. 4 now annotated to showallocated data (within the broken line circle) which is outside of theoperational window of the “high-confidence” data of the reservoirhistorical production data. The model was trained on known data to giveit the capability to identify erroneous or misallocated data.

FIG. 6 illustrates a flow diagram of one method according to the presentdisclosure;

FIG. 7 illustrates a flow diagram of another method according to thepresent disclosure

FIG. 8 illustrates a schematic view of one system according to thepresent disclosure; and

FIG. 9 illustrates a schematic view of another system according to thepresent disclosure.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS ACCORDING TO THE DISCLOSURE

The present disclosure concerns a method and system that can be used toidentify misallocated historical production data present in a trainingset of data that has been used to train a neural network algorithm toform a reservoir simulation.

Given the intractable nature of the analytical problem using only thegoverning equations of fluid mechanics, the solution to the problem ofanalyzing the flow field has been to gather copious amounts ofhistorical production data and present it to an artificial neuralnetwork (ANN). An exemplary neural network 100 comprised of multiplelayers of neurons is shown in FIG. 1. The successive layers of neuronsthat make up the ANN will establish correlations between the variousdata that are presented to it as inputs (such as geologic features(geology), separate tests, lab test, logs, reservoir properties,production rates, bottom hole pressures, PVT, GOR, and so on),effectively providing the mathematical solution to the flow field thateludes analytical solution methods and which provides an oil-wellperformance model. The types of reservoir data that can be presented tothe ANN to determine the correlations that constitute the flow fieldsolution include flow rates and flow field parameters such as pressure,volume, and temperature (PVT). The data are obtained from a multitude ofconventional sensors which are employed to monitor and/or drawproduction from the wellheads disposed throughout the reservoir. Amongother typical sensors are production logging tools (PLT). Productionlogging tools are typically modular sensors that are used downhole toprovide accurate measurements of pressure-volume-temperature (PVT), flowrates, flow velocity, gas hold up, porosity and permeability, and otherauxiliary measurements. The flow velocity measurement technologies usedby these PLTs include continuous flow meters, basket flow meters,full-bore flow meters and inline flow meters. A standard PLT will alsoinclude technologies for fluid identification and flow compositionmeasurement to obtain parameters such as gas holdup, capacitance waterholdup, radioactive fluid density, and differential pressure density.FIG. 2A shows one embodiment of an exemplary, conventional PLT with itsvariety of flow measurement instrumentation. FIG. 2B shows an embodimentof a conventional downhole well tractor tool that can be used to deploya production logging tool. The instrumentation to measure reservoirproperties can also include a multitude of other types of mechanical orelectronic downhole sensors to measure the set of well propertiesincluding pressure, temperature, fluid flow rate through each branch ofa multilateral well, as well as operational conditions such asvibration, composition, fluid flow regime, and fluid holdup. Thedownhole sensors are typically operated effectively as permanentfixtures, left in place for months or even years.

FIG. 3 shows an exemplary petroleum reservoir 300 incorporating multiplewellheads 302, wherein each wellhead comprises multiple permanent orremovable downhole sensors 304, such as production logging tools, tomeasure reservoir historical production data 306. All reservoirhistorical production data 306 gathered by the sensors 304 areposition-indexed and time-indexed over all intervals of data capture,which enables the data to be correlated in meaningful way by a systemsuch as that described by the present disclosure.

From measured data when the well is fully open, the well produces themaximum and defines the upper limit of the production. At the lowestchoke setting the well will produce the lowest and will define the lowerlimits of the production rates. The rates are then co-related topressure, GOR, etc. and the boundary conditions are established. In thisway an operational envelope for a well is generated. Once theoperational window is defined, the model is trained with the upper andlower limits of the well, and any data that is outside the boundarylimits is consider misallocated. In other words, if the rate is abovethe well's fully opened upper limit, the data is identified asmisallocated and is sent back to the pool for reallocation. A backpropagation algorithm using the trained model allocates the data to itsspecific well using the correlation it developed from differentproperties like pressure, saturation logs etc., as previously noted.

The reservoir historical production data 306 captured by the sensors 304constitutes a reservoir training set 308 to be used to train a reservoirsimulation. FIGS. 4 and 5 show an exemplary graph of reservoir sampledata. FIG. 4 shows data an interval which, in accordance with thepresent disclosure, is classified as being a “high confidence” intervalbecause conditions over the interval from 2009 to 2016 in this examplewere measured with highly sensitive instrumentation over an interval oftime, and thus are known to be representative of typical operationalconditions for the subject well. This is the dataset that is to be usedto retrain a neural network 100 to improve a predictive ability of areservoir simulation. On the other hand, the same data shown in FIG. 5has portions of data that reside outside of the operational window ofthe well in question, as shown within the broken line circle, makingthat data unsuitable for use in training a reservoir simulation as it isunrepresentative of typical operational conditions. After the reservoirsimulation has been trained, downhole sensors 304 can providehigh-confidence reservoir sample data 310 useful for comparisons 312with the reservoir historical production data 306 that was used to trainthe reservoir simulation. In this manner, notable discrepancies possiblyindicative of the presence of misallocated historical production datacan be uncovered.

FIG. 6 is a flow diagram illustrating a method 600 in accordance withthe present disclosure, wherein a reservoir simulation 602 is trained tomake predictions of reservoir performance. The method 600 begins withtraining step 604, implemented by using the reservoir training set 308to train a neural network 100 to function as a reservoir simulation 602capable of making predictions of reservoir performance. Data comprisingthe reservoir training set 308 is provided as input to a neural network100 such as that shown in FIG. 1. The neural network algorithm isimplemented by one or more processors programmed by computer code. Theneural network 100, operating as configured by the computer code,intelligently determines all position- and time-based correlationsbetween the various sensed reservoir parameters based upon the reservoirhistorical production data 306. The neural network 100 solves themultivariate problem of how changes in one flow parameter will affectall other flow parameters in the reservoir, allowing the reliableprediction of reservoir conditions when a set of initial and boundaryconditions are provided as inputs. Furthermore, if changes are made tothese initial or boundary conditions, whether on the order-of-magnitudescale or small perturbations on the supplied inputs, the trained neuralnetwork 100 of the disclosure's reservoir simulation 602 will respondwith predictions that correctly show the effects of these changes aspropagated through all corners of the simulated reservoir flow field.With the neural network so-trained with the reservoir historicalproduction data 306, the reservoir simulation is now ready to be used tomake predictions of reservoir performance.

Of course, any simulation constructed by training with a dataset ofmeasured data is only as good a predictive tool as the data it wastrained with. Therefore, any misallocated or erroneous data in thereservoir training set 308 that was used to train and developcorrelations within the neural network 100 of the reservoir simulation602 will skew the predictions made by the simulation away from correctvalues, perhaps dramatically so. For this reason, and in accordance witha salient aspect of the present disclosure, at step 606 the method 600seeks to identify such misallocated or “outlier” data by using at leastone of the sensors 304 at a reservoir location to providehigh-confidence reservoir sample data 310 over a time interval. Themethod 600 includes, at step 610, parametrically varying the reservoirhistorical production data 306 over the time interval to determine atime-indexed discrepancy 312 between the reservoir historical productiondata 306 and the high-confidence reservoir sample data 310 which hasbeen sensed over the time interval.

A system implementing the method of the present disclosure can utilizean expert system, implemented by a processor configured with codeexecuting therein, to perform the parametric variation of the reservoirhistorical production data 306 described in step 610, although othercomputer-implemented solutions can be practiced as well. The expertsystem is programmed with a knowledge base, constituted of expectedvalues for thermofluidic and geological data that would be typical of acomparable petroleum reservoir. The expert system also comprises aninference engine that includes the rules for operations on andrelationships between the data, which in the case of the petroleumreservoir includes the governing equations for conservation of mass(continuity), conservation of momentum (Navier-Stokes equations), andconservation of energy. Other parameters and effects that are specificto petroleum reservoirs, such as the effects of well spacing and depth,gas/oil ratio (GOR), as well as porosity and permeability data, are alsoencoded in the expert system.

Having been encoded as a petroleum reservoir expert system, the systemis configured by the code executing in the processor to parametricallyvary the reservoir historical production data 306 across a time intervalover which high-confidence reservoir sample data 310 has been captured,and, as it does so, compare the parametrically varied reservoirhistorical production data 306 data to high-confidence reservoir sampledata 310 at corresponding positions in the reservoir, thereby compilinga time-indexed discrepancy in the process shown as step 610.

The next step 614 of the disclosed method requires the definition of athreshold discrepancy 616 beyond which a given instance of reservoirhistorical production data becomes classified as misallocated historicalproduction data. Geologists and petroleum engineers will bewell-qualified to make the determination as to the appropriate thresholddiscrepancy for making the determination of a likely instance ofmisallocated historical production data. Using the time-indexeddiscrepancy 312 and the threshold discrepancy 616 as inputs to a machinelearning process, the neural network 100 is trained to identifyreservoir historical production data 306 that should be classified asmisallocated historical production data, wherein the discrepancy betweenthat instance of reservoir historical production data 306 and thecorresponding high-confidence reservoir sample data 310 exceeds thethreshold discrepancy 616. In an implementation of the presentdisclosure, the machine learning process comprises a neural networkalgorithm trained with high confidence data which is then presented withlow confidence historical data wherein conditions were less reliablymeasured and less comprehensively documented.

As will be understood, step 614 can, in an alternative implementationconsistent with the present disclosure, process the data and classifyreservoir historical production data as not being misallocated on thebasis of thresholds which test for being within a range. In such animplementation, data within the range is retained for the training thesimulation, and thus results in a data set comparable to thedetermination of threshold discrepancies 616 as described above.

Continuing the discussion of FIG. 6, at step 618, the identifiedmisallocated historical production data is replaced in the reservoirtraining set 308 with the high-confidence reservoir sample data toproduce a revised training set 620. Thus, the reservoir training setdefined in accordance with the technical solution described herein isnow closer to a true representation of actual performancecharacteristics of the petroleum reservoir. This provides a benefit inthat the revised training set 620 provides the means to increase theaccuracy of the reservoir simulation 602.

Step 622 of the disclosed method uses the revised training set toretrain the neural network algorithm to produce correlations in aconventional manner as other neural networks are trained.

At step 624, the process flow repeats the retraining of the neuralnetwork with the revised training set. This produces revisedcorrelations in support of improved predictions of reservoir performanceby the reservoir simulation. Step 624 can be repeated any number oftimes until the simulation's predictions show a desired fidelity to themeasurements being obtained from the reservoir sensors.

FIG. 7 is a flow diagram illustrating another method 700 in accordancewith the present disclosure, wherein a reservoir simulation is trainedusing reservoir historical production data and high-confidence reservoirsample data to identify and replace misallocated historical productiondata with high-confidence reservoir sample data, thereby improving apredictive ability of the reservoir simulation. After training theneural network with the reservoir training set and obtaininghigh-confidence reservoir sample data over a time interval, abackpropagation algorithm is then used in step 710 to compare reservoirhistorical production data to high-confidence reservoir sample data atcorresponding position and time points. A revised training set iscompiled in step 714 using the backpropagation algorithm by replacinginstances of reservoir historical production data with high-confidencereservoir sample data at corresponding position and time points at whichdifferences between the two datasets, indicating misallocated historicalproduction data, have been identified by the comparison. In step 718,the neural network algorithm is retrained using the revised training setto improve the predictive ability of the reservoir simulation.

Consistent with the disclosure, the reservoir historical production dataand the high-confidence reservoir sample data can include, withoutlimitation, observed production data, well logs, saturation logs,permeability logs, porosity logs, the product of formation permeabilityk and producing formation thickness h (kh), reservoir contact length,well spacing, choke opening, well location, well depth, well test data,well trajectories, well workover data, buildup test data, productionlogging tool data, repeat formation tester (RFT) data,pressure-volume-temperature (PVT) data, gas/oil ratio (GOR) data, coredata, special core analysis laboratory (SCAL) data, gas oil separationplant (GOSP) level, well level, well rate tests, separator tests,portable tests, and productivity/injectivity index.

FIG. 8 shows a system implementation of an embodiment 800 of the presentdisclosure. The computer-based system is comprised of at least oneprocessor 802 that is connected to at least one memory 804. The at leastone processor 802 is host to a reservoir simulation 806 comprising codeexecuting (i.e., running) on the processor. At the core of the reservoirsimulation 806 is a neural network 808, also comprising code executingin the processor, wherein the neural network is trained with a datasetreferred to as a reservoir training set 810. The data set is comprisedof reservoir physical conditions that have been obtained from thereservoir historical production data 812, and is stored in a memory,such as memory 804.

During reservoir operation, at least one sensor such as the productionlogging tool (PLT) shown in FIGS. 2A and 2B can be used at a reservoirlocation to obtain high-confidence reservoir sample data 814 over a timeinterval for use in comparisons to the reservoir historical productiondata 812 that was used to train the neural network 808 of the reservoirsimulation. Such data is stored in non-transient memory, such as thememory 804.

In an implementation of the present disclosure, an expert system 816 canbe used to make the comparisons. The expert system 816 runs on the atleast one processor 802 and is programmed with a knowledge base,constituted of expected values for thermofluidic and geological datathat would be typical of a comparable petroleum reservoir. The expertsystem 816 also comprises an inference engine that includes the rulesfor operations on and relationships between the data, which in the caseof the petroleum reservoir includes the governing equations forconservation of mass (continuity), conservation of momentum(Navier-Stokes equations), and conservation of energy. Other parametersand effects that are specific to petroleum reservoirs, such as theeffects of well spacing and depth, gas/oil ratio (GOR), as well asporosity and permeability data, are also encoded in the expert system.Having been encoded as a petroleum reservoir expert system, the systemcan then parametrically vary the reservoir historical production data812 across a time interval over which high-confidence reservoir sampledata 814 has been captured, and as it does so compare the parametricallyvaried reservoir historical production data 812 to high-confidencereservoir sample data 814 at corresponding positions in the reservoirwhile compiling a time-indexed discrepancy 818 in the process.

In an implementation of the present disclosure, a machine learningprocess 820 running on the at least one processor 802 is configured toperform further training of the reservoir simulation's neural network808.

The problem of misallocated historical production data is one that isone with which geologists, production engineers and petroleum engineersare well familiar. These petroleum professionals know the degree ofdiscrepancy between measured production parameters and the expectedparameters, based upon historical data, that would indicate a potentialproblem with misallocated historical production data. This knowledgeacquired through experience with petroleum reservoir operations enablesresponsible petroleum professionals to define a threshold discrepancy822 that, when exceeded, leads to a classification of an instance ofreservoir historical production data 812 as misallocated historicalproduction data.

The machine learning process 820 uses the time-indexed discrepancy 818and the defined threshold discrepancy 822 as inputs to further train theneural network 808, enabling it to develop the ability to identifyreservoir historical production data 812 whose discrepancy exceeds thethreshold discrepancy 822, leading said data to be classified asmisallocated historical production data. In an implementation of thepresent disclosure, the machine learning process 820 comprises a neuralnetwork.

The machine learning process 820 is further configured to replace theidentified misallocated historical production data in the reservoirsimulation training set with the high-confidence reservoir sample datato produce a revised training set 824. This replacement of data used inthe training set brings the reservoir training set closer to a truerepresentation of actual performance characteristics of the petroleumreservoir, which is beneficial in that the revised training set providesthe means to increase the accuracy of the reservoir simulation 806. Theneural network algorithm 808 is configured to be retrained to producecorrelations by using the revised training set 824 to retrain the neuralnetwork 808. The retraining of the neural network 808 with the revisedtraining set 824 can be repeated, which produces revised correlations insupport of improved predictions of reservoir performance by thereservoir simulation 806. The retraining of neural network 808 can berepeated any number of times until the simulation's predictions show adesired fidelity to the measurements being obtained from the reservoirsensors.

FIG. 9 illustrates another system in accordance with the presentdisclosure. The system incorporates a neural network algorithm trainedusing reservoir historical production data. The neural network algorithmfurther comprises a backpropagation algorithm 926 configured to comparereservoir historical production data to high-confidence reservoir sampledata from the reservoir sensors at corresponding position and timepoints. The backpropagation algorithm is also configured to compile arevised training set by replacing instances of reservoir historicalproduction data with high-confidence reservoir sample data atcorresponding position and time points at which differences between thetwo datasets, indicating misallocated historical production data, havebeen identified by the comparison. Finally, the backpropagationalgorithm is configured to retrain the neural network algorithm usingthe revised reservoir training set to improve the predictive ability ofthe reservoir simulation.

As noted above, consistent with the disclosure, the reservoir historicalproduction data and the high-confidence reservoir sample data caninclude, without limitation, observed production data, well logs,saturation logs, permeability logs, porosity logs, the product offormation permeability k and producing formation thickness h (kh),reservoir contact length, well spacing, choke opening, well location,well depth, well test data, well trajectories, well workover data,buildup test data, production logging tool data, repeat formation tester(RFT) data, pressure-volume-temperature (PVT) data, gas/oil ratio (GOR)data, core data, special core analysis laboratory (SCAL) data, gas oilseparation plant (GOSP) level, well level, well rate tests, separatortests, portable tests, and productivity/injectivity index.

From the foregoing, it will be understood that initial condition andboundary limits are derived by the algorithm from the observed datawhich are actual measurements made either in the lab or in the field.The data is co-related in form of an operational window for a well whichis derived from all measurement like geology, separate tests, lab test,logs, reservoir properties, production rates, bottom hole pressures,etc. This derived operational window is then tested with actual, knowndata which was not used in the initial training of the model and in thisway the trained model is validated. After model validation, allproduction allocation data is subjected to the foregoing chosenalgorithm and any bad allocated data outside of the limits is identified(FIG. 5) and sent back to the pool as “bad” allocated data. The poolproduction data then uses the back-propagation algorithm in view of theoperational window, open/shut status of the wells, and the trainedalgorithm in order to allocate the data to the appropriate wells. Forexample, a water status (as opposed to an oil-producing status) will beassigned to a well where saturation log shows high water saturation andnot assigned to the well in the converse case of low water saturation.Similarly, the gas volume from the pool is back-allocated to wells whichare close to the gas oil contact and when the operational windowindicates high GOR.

More particularly, in regard to the water status of a well, thesaturation logs capture information concerning where in the field thereis water saturation, that is a “high” water level. When there is waterunallocated in the pool, in accordance with the disclosure, it can beassigned to the well or wells where the saturation logs shows highwater. This back allocation for the water level constitutes a correctionof the data to improve the predictive ability of the simulation, inaccordance with the present disclosure. Likewise, whether theback-allocation is for any of the production data, any of the logs(saturation logs, well logs, permeability logs, porosity logs), or anynumber of geological or geophysical data (e.g., the product of formationpermeability k and producing formation thickness h (kh), reservoircontact length, well spacing, choke opening, well location, well depth,well test data, well trajectories, well workover data, buildup testdata, production logging tool data, repeat formation tester (RFT) data,pressure-volume-temperature (PVT) data, gas/oil ratio (GOR) data, coredata, special core analysis laboratory (SCAL) data, gas oil separationplant (GOSP) level, well level, well rate tests, separator tests,portable tests, and productivity/injectivity index), such measurementsare utilized in accordance with the disclosure in the same manner toidentify and replace misallocated historical production data and therebyimprove a predictive ability of the reservoir simulation.

The invention encompassed by the present disclosure has been describedwith reference to the accompanying drawings, which form a part hereof,and which show, by way of illustration, example implementations and/orembodiments. As such, the figures and examples above are not meant tolimit the scope of the present application to a single implementation,as other implementations are possible by way of interchange of some orall of the described or illustrated elements, without departing from thespirit of the present disclosure. Among other things, for example, thedisclosed subject matter can be embodied as methods, devices,components, or systems.

Moreover, where certain elements of the present application can bepartially or fully implemented using known components, only thoseportions of such known components that are necessary for anunderstanding of the present application are described, and detaileddescriptions of other portions of such known components are omitted soas not to obscure the application. In the present specification, animplementation showing a singular component should not necessarily belimited to other implementations including a plurality of the samecomponent, and vice-versa, unless explicitly stated otherwise herein.Moreover, applicants do not intend for any term in the specification orclaims to be ascribed an uncommon or special meaning unless explicitlyset forth as such. Further, the present application encompasses presentand future known equivalents to the known components referred to hereinby way of illustration.

Furthermore, it is recognized that terms used herein can have nuancedmeanings that are suggested or implied in context beyond an explicitlystated meaning. Likewise, the phrase “in one embodiment” as used hereindoes not necessarily refer to the same embodiment and the phrase “inanother embodiment” as used herein does not necessarily refer to adifferent embodiment. It is intended, for example, that claimed subjectmatter can be based upon combinations of individual example embodiments,or combinations of parts of individual example embodiments.

The foregoing description of the specific implementations will so fullyreveal the general nature of the application that others can, byapplying knowledge within the skill of the relevant art(s) (includingthe contents of the documents cited and incorporated by referenceherein), readily modify and/or adapt for various applications suchspecific implementations, without undue experimentation, withoutdeparting from the general concept of the present application. Suchadaptations and modifications are therefore intended to be within themeaning and range of equivalents of the disclosed implementations, basedon the teaching and guidance presented herein. It is to be understoodthat the phraseology or terminology herein is for the purpose ofdescription and not of limitation, such that the terminology orphraseology of the present specification is to be interpreted by theskilled artisan in light of the teachings and guidance presented herein,in combination with the knowledge of one skilled in the relevant art(s).It is to be understood that dimensions discussed or shown of drawingsare shown accordingly to one example and other dimensions can be usedwithout departing from the present disclosure.

While various implementations of the present application have beendescribed above, it should be understood that they have been presentedby way of example, and not limitation. It would be apparent to oneskilled in the relevant art(s) that various changes in form and detailcould be made therein without departing from the spirit and scope of thedisclosure. Thus, the present disclosure should not be limited by any ofthe above-described example implementations, and the invention is to beunderstood as being defined by the recitations in the claims whichfollow and structural and functional equivalents of the features andsteps in those recitations.

What is claimed:
 1. A computer-based method of training a reservoirsimulation using reservoir historical production data andhigh-confidence reservoir sample data to identify misallocatedhistorical production data and thereby improve a predictive ability ofthe reservoir simulation, the method comprising: training a neuralnetwork algorithm with a reservoir training set to produce correlationsthat enable the reservoir simulation to make predictions of reservoirperformance, wherein the reservoir training set comprises reservoirphysical conditions obtained from the reservoir historical productiondata; obtaining high-confidence reservoir sample data which is sensedover a time interval using at least one portable sensor or at least onesensor at a reservoir location; parametrically varying the reservoirhistorical production data over the time interval to determine atime-indexed discrepancy between the reservoir historical productiondata and the high-confidence reservoir sample data which has been sensedover the time interval at corresponding locations in the reservoir;using the time-indexed discrepancy and a defined threshold discrepancyas inputs to a machine learning process to further train the neuralnetwork algorithm to identify reservoir historical production data whosediscrepancy exceeds the threshold discrepancy and thereby constitutesmisallocated historical production data; and replacing the identifiedmisallocated historical production data in the reservoir training setwith the high-confidence reservoir sample data to produce a revisedtraining set. A machined is trained with known measurements of highconfidence and an operational window for a well is established. Themodel is than subjected to the whole life of the well historical dataand any data which fall out of the operational limits of the well areidentified as misallocated data.
 2. The method as in claim 1, furthercomprising retraining the neural network algorithm to producecorrelations using the revised training set as input to the neuralnetwork.
 3. The method as in claim 2, wherein the training step of claim1 is repeated with the revised training set, producing revisedcorrelations in support of improved predictions of reservoir performanceby the reservoir simulation.
 4. The method as in claim 1, wherein themachine learning process comprises a neural network algorithm.
 5. Acomputer-based system used to train a reservoir simulation usinghigh-confidence reservoir historical production data and high-confidencereservoir sample data to identify misallocated historical productiondata for improvement of a predictive ability of the reservoirsimulation, the system comprising: at least one processor connected toat least one memory; a reservoir simulation running on the at least oneprocessor, the reservoir simulation comprising a neural networkalgorithm trained with a reservoir training set to produce correlationsthat enable the reservoir simulation to make predictions of reservoirperformance, wherein the reservoir training set comprises reservoirphysical conditions obtained from the high-confidence reservoirhistorical production data; at least one portable sensor or at least onesensor at a reservoir location to provide high-confidence reservoirsample data over a time interval; an expert system running on the atleast one processor, the expert system configured to parametrically varythe reservoir historical production data over the time interval todetermine a time-indexed discrepancy between the reservoir historicalproduction data and the high-confidence reservoir sample data which hasbeen sensed over the time interval at corresponding locations in thereservoir; a machine learning process running on the at least oneprocessor, the machine learning process configured to use thetime-indexed discrepancy and a defined threshold discrepancy as inputsto further train the neural network algorithm to identify reservoirhistorical production data whose discrepancy exceeds the thresholddiscrepancy and thereby constitutes misallocated historical productiondata, the machine learning process further configured to replace theidentified misallocated historical production data in the reservoirsimulation training set with the high-confidence reservoir sample datato produce a revised training set.
 6. The system as in claim 5, whereinthe neural network algorithm is configured to be retrained to producecorrelations by using the revised training set as input to the neuralnetwork.
 7. The system as in claim 6, wherein the step of training theneural network of claim 5 is repeated with the revised training set,producing revised correlations in support of improved predictions ofreservoir performance by the reservoir simulation.
 8. The system as inclaim 5, wherein the machine learning process comprises a neural networkalgorithm.
 9. A computer-based method of training a reservoir simulationusing reservoir historical production data and high-confidence reservoirsample data to identify and replace misallocated historical productiondata with high-confidence reservoir sample data, thereby improving apredictive ability of the reservoir simulation, the method comprising:using high-confidence reservoir historical production data as a trainingset to train a neural network algorithm to function as a reservoirsimulation capable of predicting values for reservoir production data,wherein the reservoir historical production data has been obtained overan interval of time from a plurality of sensors distributed throughoutthe reservoir; obtaining high-confidence reservoir sample data using atleast one portable sensor or at least one of the plurality of sensorsdistributed throughout the reservoir; and using a backpropagationalgorithm to: compare reservoir historical production data tohigh-confidence reservoir sample data at corresponding position and timepoints; compile a revised training set by replacing instances ofreservoir historical production data with high-confidence reservoirsample data at corresponding position and time points at whichdifferences between the two datasets, indicating misallocated historicalproduction data, have been identified by the comparison; and retrain theneural network algorithm using the revised training set to improve thepredictive ability of the reservoir simulation.
 10. The method as inclaim 9, wherein the reservoir historical production data and thehigh-confidence reservoir sample data comprise: observed productiondata, well logs, saturation logs, permeability logs, porosity logs,saturation logs, the product of formation permeability k and producingformation thickness h (kh), reservoir contact length, well spacing,choke opening, well location, well depth, well test data, welltrajectories, well workover data, buildup test data, production loggingtool data, repeat formation tester (RFT) data,pressure-volume-temperature (PVT) data, gas/oil ratio (GOR) data, coredata, special core analysis laboratory (SCAL) data, gas oil separationplant (GOSP) level, well level, well rate tests, separator tests,portable tests, and productivity/injectivity index.
 11. A computer-basedsystem used to train a reservoir simulation using reservoir historicalproduction data and high-confidence reservoir sample data to identifymisallocated historical production data and replace misallocatedhistorical production data with high-confidence reservoir sample data,thereby improving a predictive ability of the reservoir simulation, thesystem comprising: a plurality of sensors distributed throughout thereservoir to obtain reservoir historical production data over aninterval of time; at least one processor connected to at least onememory; a reservoir simulation running on the at least one processor,the reservoir simulation comprising: a neural network algorithm trainedwith a reservoir training set comprising the reservoir historicalproduction data to enable the neural network algorithm to function as areservoir simulation capable of predicting values for reservoirproduction data, wherein the neural network algorithm further comprisesa backpropagation algorithm configured to: compare reservoir historicalproduction data to high-confidence reservoir sample data atcorresponding position and time points, the high-confidence reservoirsample data obtained using at least one portable sensor or at least oneof the plurality of sensors distributed throughout the reservoir;compile a revised training set by replacing instances of reservoirhistorical production data with high-confidence reservoir sample data atcorresponding position and time points at which differences between thetwo datasets, indicating misallocated historical production data, havebeen identified by the comparison; and retrain the neural networkalgorithm using the revised reservoir training set to improve thepredictive ability of the reservoir simulation. The misallocated data issend back to the pool of misallocated data and using the backpropagation algorithm it is assigned to the wells which has thepotential of producing such results. E.g, if the saturation log does notshow water but the production well shows water produced than the backpropagation algorithm will track the produced water to the well whosaturation logs with time indicate possible water breakthroughs.
 12. Thesystem as in claim 11, wherein the reservoir historical production dataand the high-confidence reservoir sample data comprise: observedproduction data, well logs, saturation logs, permeability logs, porositylogs, saturation logs, the product of formation permeability k andproducing formation thickness h (kh), reservoir contact length, wellspacing, choke opening, well location, well depth, well test data, welltrajectories, well workover data, buildup test data, production loggingtool data, repeat formation tester (RFT) data,pressure-volume-temperature (PVT) data, gas/oil ratio (GOR) data, coredata, special core analysis laboratory (SCAL) data, gas oil separationplant (GOSP) level, well level, well rate tests, separator tests,portable tests, and productivity/injectivity index.